config.target_class = train_iter.dataset.NUM_CLASSES
config.words_num = len(train_iter.dataset.TEXT_FIELD.vocab)
print('Dataset:', args.dataset)
print('No. of target classes:', train_iter.dataset.NUM_CLASSES)
print('No. of train instances', len(train_iter.dataset))
print('No. of dev instances', len(dev_iter.dataset))
print('No. of test instances', len(test_iter.dataset))
if args.resume_snapshot:
if args.cuda:
model = torch.load(args.resume_snapshot, map_location=lambda storage, location: storage.cuda(args.gpu))
else:
model = torch.load(args.resume_snapshot, map_location=lambda storage, location: storage)
else:
model = XmlCNN(config)
model.to(device)
if not args.trained_model:
save_path = os.path.join(args.save_path, dataset_map[args.dataset].NAME)
os.makedirs(save_path, exist_ok=True)
parameter = filter(lambda p: p.requires_grad, model.parameters())
optimizer = torch.optim.Adam(parameter, lr=args.lr, weight_decay=args.weight_decay)
train_evaluator = EvaluatorFactory.get_evaluator(dataset_map[args.dataset], model, None, train_iter, args.batch_size, args.gpu)
test_evaluator = EvaluatorFactory.get_evaluator(dataset_map[args.dataset], model, None, test_iter, args.batch_size, args.gpu)
dev_evaluator = EvaluatorFactory.get_evaluator(dataset_map[args.dataset], model, None, dev_iter, args.batch_size, args.gpu)
if hasattr(train_evaluator, 'is_multilabel'):
train_evaluator.is_multilabel = dataset_class.IS_MULTILABEL
else:
dataset_class = dataset_map[args.dataset]
print('Dataset:', args.dataset)
print('No. of target classes:', train_iter.dataset.NUM_CLASSES)
print('No. of train instances', len(train_iter.dataset))
print('No. of dev instances', len(dev_iter.dataset))
print('No. of test instances', len(test_iter.dataset))
if args.resume_snapshot:
if args.cuda:
model = torch.load(args.resume_snapshot, map_location=lambda storage, location: storage.cuda(args.gpu))
else:
model = torch.load(args.resume_snapshot, map_location=lambda storage, location: storage)
else:
model = XmlCNN(config)
if args.cuda:
model.cuda()
if not args.trained_model:
save_path = os.path.join(args.save_path, dataset_map[args.dataset].NAME)
os.makedirs(save_path, exist_ok=True)
parameter = filter(lambda p: p.requires_grad, model.parameters())
optimizer = torch.optim.Adam(parameter, lr=args.lr, weight_decay=args.weight_decay)
train_evaluator = EvaluatorFactory.get_evaluator(dataset_map[args.dataset], model, None, train_iter, args.batch_size, args.gpu)
test_evaluator = EvaluatorFactory.get_evaluator(dataset_map[args.dataset], model, None, test_iter, args.batch_size, args.gpu)
dev_evaluator = EvaluatorFactory.get_evaluator(dataset_map[args.dataset], model, None, dev_iter, args.batch_size, args.gpu)
if hasattr(train_evaluator, 'is_multilabel'):
print('Dataset:', args.dataset)
print('No. of target classes:', train_iter.dataset.NUM_CLASSES)
print('No. of train instances', len(train_iter.dataset))
print('No. of dev instances', len(dev_iter.dataset))
print('No. of test instances', len(test_iter.dataset))
if args.resume_snapshot:
if args.cuda:
model = torch.load(
args.resume_snapshot,
map_location=lambda storage, location: storage.cuda(args.gpu))
else:
model = torch.load(args.resume_snapshot,
map_location=lambda storage, location: storage)
else:
model = XmlCNN(config)
if args.cuda:
model.cuda()
if not args.trained_model:
save_path = os.path.join(args.save_path,
dataset_map[args.dataset].NAME)
os.makedirs(save_path, exist_ok=True)
parameter = filter(lambda p: p.requires_grad, model.parameters())
optimizer = torch.optim.Adam(parameter,
lr=args.lr,
weight_decay=args.weight_decay)
train_evaluator = EvaluatorFactory.get_evaluator(dataset_map[args.dataset],
model, None, train_iter,